Few-shot learning is a machine learning technique where a model is trained on a small number of examples from each class, usually only one or a few examples. This is in contrast to traditional machine learning techniques which require large amounts of labeled data for training. The goal of few-shot learning is to quickly learn new classes with minimal training, allowing the model to quickly adapt to new tasks.